With the global emphasis on environmental sustainability, particularly in the context of industrial manufacturing, and the need to comply with government regulations, organizations have been compelled to adopt sustainable manufacturing processes and report their current sustainability levels and strategic goals. The organizations are using multicriteria decision-making frameworks to assess their current sustainability performance and prioritize key elements for strategic decision-making. However, the current sustainability assessment frameworks involve complex and laborious procedures, starting with the collection of data from organizational databases and then computing their sustainability level. In this regard, the study presents a structured application for calculating the organizational sustainability index (SI). The developed application utilizes the Analytical Hierarchy Process (AHP) and an aggregation method to calculate the overall sustainability index. The integration of the application with the organizational database enables seamless calculation throughout the entire process. The application provides a better understanding of sustainability performance and enhances organizational strategic decision-making by prioritizing the most influential sustainability indicators.

This paper introduces a multi-method explainable artificial intelligence framework designed as a foundational step toward European Union AI Act-compliant automated ICD-10 coding. The framework integrates three complementary explainability methods: label-wise attention, SHAP and case-based reasoning. Unlike existing approaches that typically employ single explanation methods, this new framework creates a comprehensive implementation of multiple explainable techniques while implementing stratified evaluation across different data complexity levels. The architecture leverages a transformer-based model with label-wise attention aggregation. While current performance levels require further development before industry deployment, preliminary results demonstrate competitive performance with micro-F1 score of 0.565 and explanation coverage of 87.1%, establishing critical infrastructure for regulatory-compliant explainable AI in healthcare.

Supply chain interruptions impact all sizes of organizations, but small and medium-sized enterprises (SMEs) are particularly susceptible since they have fewer resources and less negotiating leverage than larger corporations. Environmental disruptions such as climate change, resource scarcity, and natural disasters lead to operational delays, higher costs, and revenue loss. SMEs can mitigate these disruptions by collaborating with their supply chain partners. This study presents a framework for how modeling simulation and artificial intelligence can aid in collaboration to support SMEs in mitigating the operational impacts of environmental supply chain disruptions. It indicates that partnerships can assist SMEs in creating contingency plans, sharing resources, and building resilience. It identifies key benefits of collaboration for SMEs and different strategies that SMEs can use to mitigate the disruptions due to environmental issues. The paper concludes by calling for more research on developing a decision-making framework to overcome collaboration barriers faced by SMEs.

This literature review examines recent studies on the application of artificial intelligence (AI) in public administration, incorporating real-life examples to demonstrate the impact of AI on various aspects of public administration. The review is organised into the following sections: AI for decision-making, AI for public service delivery, AI for policy analysis, AI for public engagement, and AI for public sector efficiency. The paper concludes by identifying potential challenges and future research directions.

The paper has its focus on the creation of an innovative Natural Language Processing system for the quest of available information and consequent data analysis, aimed at reconstructing the corporate chain and monitoring the sensitive risk of corruption for people involved in command positions. Today, the greatest opportunity in finding information is represented by the Internet or other open sources, where the contents related to corporate managers are continuously posted and updated. Given the vastness of the information dimension, it seems remarkably advantageous to have an intelligent analysis system capable of independently finding, analyzing and synthesizing information related to a set of target subjects. The aim of this document is to describe a forecasting model based on Machine Learning and Artificial Intelligence techniques capable of understanding whether a news item related to an individual (sought during a due diligence process) contains information about crime, investigation, conviction, fraud, corruption or sanction relating to the subject sought. Methods based on Artificial Neural Networks and Support Vector Machine, compared one to the others, are introduced and applied for the scope. In particular, results showed the architecture based on SVM with TF-IDF matrix and test pre-processing outperforms the others discussed in this paper demonstrating high accuracy and precision in prediction new data as well.

In the last years, there has been growing a large increase in digital imaging techniques, and their applications became more and more pivotal in many critical scenarios. Conversely, hand in hand with this technological boost, imaging forgeries have increased more and more along with their level of precision. In this view, the use of digital tools, aiming to verify the integrity of a certain image, is essential. Indeed, insurance is a field that extensively uses images for filling claim requests and a robust forgery detection is essential. This paper proposes an approach which aims to introduce a full-automated system for identifying potential splicing frauds in images of car plates by overcoming traditional problems using artificial neural networks (ANN). For instance, classic fraud-detection algorithms are impossible to fully automatize whereas modern deep learning approaches require vast training datasets that are not available most of the time. The method developed in this paper uses Error Level Analysis (ELA) performed on car license plates as an input for a trained model which is able to classify license plates in either original or forged.

The present paper proposes an innovative system architecture for the safety management of passenger evacuation inside an Airport Terminal, in case of a big indoor fire. The basic idea, in addition to fire hazard pre-assessment, is that information from a fast-predictive simulation of the fire evolution, immediately after the fire starting, could help the airport safety management system in taking sudden decisions to manage very specific fire scenarios. The system is based on an advanced technological interconnection among a simulation model of Fire Safety Engineering, IoT safety and environmental sensors, specific Augmented Reality equipment, and a remote server, able to exchange data by Wi-Fi connections and to elaborate them on a software platform. The ultimate scope of this system is to equip rescuers and airport safety managers with added value AR tools, like AR smart-glasses or tablets, usable for supporting safety decisions and emergency interventions.

Healthcare and more precisely private hospitals are critical and complex environments where making appropriate decisions is vital. For this reason, they are widely studied in many fields. This paper aims to provide the current state of the art of Digital Twin and/or Simulation involved in Decision Support System (DSS) whose data are processed through Data Mining techniques applied in the healthcare sector. In this view, the authors' research has been based on the following keywords: Healthcare, Hospital, Digital Twin, Simulation, Data Mining and Decision Support System. Doing so, it has been possible to gather 13 papers which have been carefully studied.

This paper presents a scheduler built around a set of Commercial Off of the Shelf (COTS) functions where the overall complexity have been reduced by a set of pre-processing and post-processing functions. This approach offers many advantages compared to design-for purpose heuristics in term of time to market as well as in term of design and implementation costs.

This paper describes an application of the cutting stock problem where great part of the complexity of the problem was actively addressed by mean of a definition of a suitable ontology. It deals with the realization of a series of concentric rings starting from a raw circular flange. The goal is to minimize the scrap and satisfy the customers demand, keeping into account all the constraints related to the coupling of rings and flanges. To reach the goal a combinatory optimization approach was employed. The mathematical model is solved by the LINGO software, an interactive tool for the solution of linear, quadratic and integer number programming problems.

Simulation is the best tool used for any nontrivial, real world system and many simulation tools are today available for supporting complex system modeling. In general any simulation language or tool is suitable for any application however some approaches could provide easier and better results than others. This paper provides some practical suggestion specifically for maritime operations.

The purpose of this study is undertake a review of the seaborne coal supply chains for two important Power Stations in the Mediterranean Sea. We are considered four different scenarios. An important aspect of this study has been the consultation with the coal supply chain participants, involving interviews, site visits and workshops, to share present information, medium term plans, objectives and expectations. The design of experiment (DOE) approach has been employed. DOE is important as a formal way of maximizing information gained by available resources. It has more to offer than “one change at a time” experimental methods because it allows a judgment on the significance to the output of input variables acting alone, as well as input variables acting in combination with one another. Using the results of the simulations, a regression analysis has been performed, providing multi-dimensional response surfaces expressing the dependency of throughput and demurrage days on the independent variables considered. The results of simulations are described at paragraph at the end of paper.

The purpose of this study is undertake a review of the seaborne coal supply chains for two important Power Stations in the Mediterranean Sea (associated at two important Companies). We have studied four scenarios. Each scenario has been developed in terms of: ocean freight assessment and preliminary operative costs estimation. The purpose of this study is to identify, analyse and make recommendations on key issues and potential bottlenecks that might result in capacity constraints and/or supply chain inefficiency thus leading in unnecessary additional costs of the coal delivered to the Power Plants.

The aim of the paper is to describe a project concerned with the development of a daily monitoring system for elderly people living alone. The system relies on a new non invasive type of communication based on devices commonly owned by elderly people, to reduce initial cost of deployment. All collected data could then be analyzed by a Medical Doctor to monitor the real current situation of the patient using open source instrument to generate analysis, report and data mining tasks.

The raise of BRICs countries has been regarded recently as an opportunity for cost reduction from western companies resulting in a pressure to increase of the delocalization of the production process with the consequence of increasing of the management complexity requiring specific ERP systems able to deal with new and improved process. Every company has business processes in order to manufacture products, to provide services, to purchase goods and even to maintain plants and company's assets; the basic of every change is the knowledge of the process and the understanding the possible evolution. In nowadays systems information systems and cloud infrastructures record events; these events can be used to make processes visible and thus a modeling framework may provide the insights necessary to manage, control, and improve processes. This paper present a general approach and a set of case studies where Computational Intelligence may assist the “self modeling” of complex processes.